Classification and identification of tempered flexural organization of spring steel based on improved GLCM-Resnet18
Bibliographic record
Abstract
Metallographic structure is generally judged by professionals based on existing knowledge and work experience, and the judgment results are somewhat subjective.In recent years, convolutional neural network (CNN) in deep learning methods can learn complex features in original images, widely used in the field of image classification and recognition.However, CNN require a large number of sample training to achieve good prediction results.In order to make up for the shortcomings of the subjectivity of manual judgment, and the problem that the data sets for specific problems in the field of materials engineering are often small, this study uses the grey level co-occurrence matrix (GLCM) to count the texture features of the original image, and then uses the standard Resnet18, Resnet50 and improved Resnet18 frameworks for migration training to classify and identify the grey level co-occurrence matrix of the troostite structure, in order to solve the problem of small metallographic image data sets and realize deep learning modeling of small samples.Using 490 microstructure images of spring steel tempered troostite collected by professional technicians, and each level have 98 images.The grey level co-occurrence matrix is used to count its texture information, thereby obtaining the training data set.The experimental results on this dataset show that the classification accuracy of the improved GLCM-Resnet18 can reach up to 96.52%, the highest accuracy of GLCM-Resnet18 is 95.65%, and the highest accuracy of GLCM-Resnet50 is 90.72%.It can be considered that the improved GLCM-Resnet18 method has more precise training accuracy and can basically meet the requirements of industrial applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".